Generating Personalized Explanations for Recommender Systems Using a Knowledge Base
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- Yuhao Chen
- Tokyo Institute of Technology, Japan
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- Shi-Jun Luo
- Tokyo Institute of Technology, Japan
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- Hyoil Han
- Illinois State University, USA
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- Jun Miyazaki
- Tokyo Institute of Technology, Japan
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- Alfrin Letus Saldanha
- Illinois State University, USA
抄録
<jats:p>In the last decade, we have seen an increase in the need for interpretable recommendations. Explaining why a product is recommended to a user increases user trust and makes the recommendations more acceptable. The authors propose a personalized explanation generation system, PEREXGEN (personalized explanation generation) that generates personalized explanations for recommender systems using a model-agnostic approach. The proposed model consists of a recommender and an explanation module. Since they implement a model-agnostic approach to generate personalized explanations, they focus more on the explanation module. The explanation module consists of a task-specialized item knowledge graph (TSI-KG) generation from a knowledge base and an explanation generation component. They employ the MovieLens and Wikidata datasets and evaluate the proposed system's model-agnostic properties using conventional and state-of-the-art recommender systems. The user study shows that PEREXGEN generates more persuasive and natural explanations.</jats:p>
収録刊行物
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- International Journal of Multimedia Data Engineering and Management
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International Journal of Multimedia Data Engineering and Management 12 (4), 20-37, 2021-10
IGI Global
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詳細情報 詳細情報について
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- CRID
- 1360861287039217920
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- ISSN
- 19478542
- 19478534
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- データソース種別
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- Crossref
- KAKEN